⚡ AI-Lesson Articles

arXiv:2602.13458v2 Announce Type: replace-cross Abstract: Large-scale communities of AI agents are becoming increasingly prevalent, creating new environments fo

arXiv:2602.14351v2 Announce Type: replace-cross Abstract: Model-based reinforcement learning promises strong sample efficiency but often underperforms in practi

arXiv:2602.14536v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have seen remarkable advancements, achieving state-of-the-art results in

arXiv:2602.16813v2 Announce Type: replace-cross Abstract: Language models based on discrete diffusion have attracted widespread interest for their potential to

arXiv:2603.00077v2 Announce Type: replace-cross Abstract: Techniques for reliable rubric-based LLM evaluation -- ensemble judging, bias mitigation, few-shot cal

arXiv:2603.02050v3 Announce Type: replace-cross Abstract: Human collaborators coordinate dynamically through process visibility and workspace awareness, yet AI

arXiv:2603.02293v2 Announce Type: replace-cross Abstract: While implicit regularization facilitates benign overfitting in low-noise regimes, recent theoretical

arXiv:2603.03684v3 Announce Type: replace-cross Abstract: Recent developments show that AI can prove research-level theorems in mathematics, both formally and i

arXiv:2603.06977v2 Announce Type: replace-cross Abstract: Multi-agent reinforcement learning (MARL) is increasingly used to design learning-enabled agents that

arXiv:2603.09030v3 Announce Type: replace-cross Abstract: Action-conditioned video models offer a promising path to building general-purpose robot simulators th

arXiv:2603.10047v2 Announce Type: replace-cross Abstract: Hallucinations in large language models (LLMs) are outputs that are syntactically coherent but factual

arXiv:2603.11321v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for post-tra

arXiv:2603.11749v3 Announce Type: replace-cross Abstract: Why do language models trained on contradictory data prefer correct answers? In controlled experiments

arXiv:2603.12230v2 Announce Type: replace-cross Abstract: This article, a lightly adapted version of Perplexity's response to NIST/CAISI Request for Information

arXiv:2603.13285v2 Announce Type: replace-cross Abstract: Existing evaluation methods largely rely on clean, static benchmarks, which can overestimate true mode

arXiv:2603.13343v2 Announce Type: replace-cross Abstract: Predictive maintenance for connected vehicles offers the potential to reduce unexpected breakdowns and

arXiv:2603.13777v2 Announce Type: replace-cross Abstract: Aspect-based sentiment analysis (ABSA) extracts aspect-level sentiment signals from user-generated tex

arXiv:2603.13842v2 Announce Type: replace-cross Abstract: End-to-end autonomous driving is typically built upon imitation learning (IL), yet its performance is

arXiv:2604.01413v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) increasingly rely on multi-turn reasoning and interaction, such as adapti

arXiv:2604.01676v2 Announce Type: replace-cross Abstract: GUI Process Automation (GPA) is a lightweight but general vision-based Robotic Process Automation (RPA

After Xara went viral, its Nigerian founder caught the attention of Elon Musk’s xAI. Built as a WhatsApp banking assistant, the startup now has 45,000 users and

Microsoft ships Agent Framework 1.0 but Azure's agent stack still spans too many surfaces while Google and AWS offer cleaner developer paths.

Passwords are not dying only because technology is changing. They are dying because the old model of digital trust no longer fits the world we built.

Data disruptions are no longer rare events. Hardware failures, ransomware, and unexpected outages can interrupt operations at any time. The difference between a